Approximately estimates a PCA model for dimensionality reduction based on an input dataset.
Performs dimensionality reduction on an input dataset where each input item is an NxD array and the projection matrix is a DxK array.
Column-wise block implementation of a kernel matrix.
Fits a least squares model using block coordinate descent with provided training features and labels
Transformer that applies a linear model to an input.
Train a weighted block-coordinate descent model using least squares
Estimates a PCA model for dimensionality reduction based on a sample of a larger input dataset.
A trait that represents a known system performance cost model for a solver.
Class used to solve an optimization problem using Limited-memory BFGS.
Estimates a PCA model for dimensionality reduction based on a sample of a larger input dataset, using a distributed PCA algorithm.
Estimates a PCA model for dimensionality reduction using a distributedQR.
Gaussian (RBF) kernel generator.
A Mixture of Gaussians, usually computed via some clustering process.
Fit a Gaussian Mixture model to Data.
Computes a gradient given vectors as data, labels, and model weights
A KMeans assigning transformer
Trains a k-means++ transformer
Transformer that applies a kernel model to an input.
Base trait for functions that compute a kernel on a given dataset.
Defines a wrapper to access elements of a symmetric distributed matrix that is generated using a kernel function.
Solves a kernel ridge regression problem of the form (K(x, x) + \lambda * I) * W = Y using Gauss-Seidel based Block Coordinate Descent.
Base trait for functions that transform the given input data with respect to a pre-specified kernel function.
Computes a Least Squares loss gradient given DenseVectors as data
A least squares solver that is optimized to use a fast algorithm, based on characteristics of the workload and cost models.
Computes a Least Squares loss gradient given SparseVectors as data
An Estimator that fits Linear Discriminant Analysis (currently not calculated in a distributed fashion), and returns a transformer that projects into the new space
Linear Map Estimator.
Computes A * x + b i.
Estimates a PCA model for dimensionality reduction based on a sample of a larger input dataset.
Learns a linear model (OLS) based on training features and training labels.
A LabelEstimator which learns a Logistic Regression model from training data.
A Logistic Regression model that transforms feature vectors to vectors containing the logistic regression output of the different classes
A LabelEstimator which learns a multinomial naive bayes model from training data.
A Multinomial Naive Bayes model that transforms feature vectors to vectors containing the log posterior probabilities of the different classes
Estimates a PCA model for dimensionality reduction based on a sample of a larger input dataset.
Performs dimensionality reduction on an input dataset.
Train a weighted block-coordinate descent model using least squares
Class used to solve an optimization problem using Limited-memory BFGS.
Computes A * x + b i.
Computes a ZCA Whitener, which is intended to rotate an input dataset to identity covariance.
Companion object to LinearMapEstimator that allows for construction without new.
Randomly distributes the initial means within the minimum and maximum values seen in the training data, using a uniform distribution.